gtfintechlab/model_bank_of_thailand_certain_label

World of Central Banks Model

Model Name: Bank of Thailand Uncertainty Estimation Model

Model Type: Text Classification

Language: English

License: CC-BY-NC-SA 4.0

Base Model: bert-base-uncased

Dataset Used for Training: gtfintechlab/bank_of_thailand

Model Overview

Bank of Thailand Uncertainty Estimation Model is a fine-tuned bert-base-uncased model designed to classify text data on Uncertain Estimation. This label is annotated in the bank_of_thailand dataset, which focuses on meeting minutes for the Bank of Thailand.

Intended Use

This model is intended for researchers and practitioners working on subjective text classification for the Bank of Thailand, particularly within financial and economic contexts. It is specifically designed to assess the Uncertain Estimation label, aiding in the analysis of subjective content in financial and economic communications.

How to Use

To utilize this model, load it using the Hugging Face transformers library:

from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig

# Load tokenizer, model, and configuration
tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/bank_of_thailand", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/bank_of_thailand", num_labels=2)
config = AutoConfig.from_pretrained("gtfintechlab/bank_of_thailand")

# Initialize text classification pipeline
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, framework="pt")

# Classify Uncertain Estimation
sentences = [
    "[Sentence 1]",
    "[Sentence 2]"
]
results = classifier(sentences, batch_size=128, truncation="only_first")

print(results)

In this script:

• Tokenizer and Model Loading:
  Loads the pre-trained tokenizer and model from gtfintechlab/bank_of_thailand.

• Configuration:
  Loads model configuration parameters, including the number of labels.

• Pipeline Initialization:
  Initializes a text classification pipeline with the model, tokenizer, and configuration.

• Classification:
  Labels sentences based on Uncertain Estimation.

Ensure your environment has the necessary dependencies installed.

Label Interpretation

• LABEL_0: Certain; indicates that the sentence presents information definitively.
• LABEL_1: Uncertain; indicates that the sentence presents information with speculation, possibility, or doubt.

Training Data

The model was trained on the bank_of_thailand dataset, comprising annotated sentences from the Bank of Thailand meeting minutes, labeled by Uncertain Estimation. The dataset includes training, validation, and test splits.

Citation

If you use this model in your research, please cite the bank_of_thailand:

@article{WCBShahSukhaniPardawala,
  title={Words That Unite The World: A Unified Framework for Deciphering Global Central Bank Communications},
  author={Agam Shah, Siddhant Sukhani, Huzaifa Pardawala et al.},
  year={2025}
}

For more details, refer to the bank_of_thailand dataset documentation.

Contact

For any bank_of_thailand related issues and questions, please contact:

• Huzaifa Pardawala: huzaifahp7[at]gatech[dot]edu

• Siddhant Sukhani: ssukhani3[at]gatech[dot]edu

• Agam Shah: ashah482[at]gatech[dot]edu